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spaCy/website/docs/api/vocab.md
Connor Brinton 657af5f91f
🏷 Add Mypy check to CI and ignore all existing Mypy errors (#9167) 
* 🚨 Ignore all existing Mypy errors

* 🏗 Add Mypy check to CI

* Add types-mock and types-requests as dev requirements

* Add additional type ignore directives

* Add types packages to dev-only list in reqs test

* Add types-dataclasses for python 3.6

* Add ignore to pretrain

* 🏷 Improve type annotation on `run_command` helper

The `run_command` helper previously declared that it returned an
`Optional[subprocess.CompletedProcess]`, but it isn't actually possible
for the function to return `None`. These changes modify the type
annotation of the `run_command` helper and remove all now-unnecessary
`# type: ignore` directives.

* 🔧 Allow variable type redefinition in limited contexts

These changes modify how Mypy is configured to allow variables to have
their type automatically redefined under certain conditions. The Mypy
documentation contains the following example:

```python
def process(items: List[str]) -> None:
    # 'items' has type List[str]
    items = [item.split() for item in items]
    # 'items' now has type List[List[str]]
    ...
```

This configuration change is especially helpful in reducing the number
of `# type: ignore` directives needed to handle the common pattern of:
* Accepting a filepath as a string
* Overwriting the variable using `filepath = ensure_path(filepath)`

These changes enable redefinition and remove all `# type: ignore`
directives rendered redundant by this change.

* 🏷 Add type annotation to converters mapping

* 🚨 Fix Mypy error in convert CLI argument verification

* 🏷 Improve type annotation on `resolve_dot_names` helper

* 🏷 Add type annotations for `Vocab` attributes `strings` and `vectors`

* 🏷 Add type annotations for more `Vocab` attributes

* 🏷 Add loose type annotation for gold data compilation

* 🏷 Improve `_format_labels` type annotation

* 🏷 Fix `get_lang_class` type annotation

* 🏷 Loosen return type of `Language.evaluate`

* 🏷 Don't accept `Scorer` in `handle_scores_per_type`

* 🏷 Add `string_to_list` overloads

* 🏷 Fix non-Optional command-line options

* 🙈 Ignore redefinition of `wandb_logger` in `loggers.py`

* ➕ Install `typing_extensions` in Python 3.8+

The `typing_extensions` package states that it should be used when
"writing code that must be compatible with multiple Python versions".
Since SpaCy needs to support multiple Python versions, it should be used
when newer `typing` module members are required. One example of this is
`Literal`, which is available starting with Python 3.8.

Previously SpaCy tried to import `Literal` from `typing`, falling back
to `typing_extensions` if the import failed. However, Mypy doesn't seem
to be able to understand what `Literal` means when the initial import
means. Therefore, these changes modify how `compat` imports `Literal` by
always importing it from `typing_extensions`.

These changes also modify how `typing_extensions` is installed, so that
it is a requirement for all Python versions, including those greater
than or equal to 3.8.

* 🏷 Improve type annotation for `Language.pipe`

These changes add a missing overload variant to the type signature of
`Language.pipe`. Additionally, the type signature is enhanced to allow
type checkers to differentiate between the two overload variants based
on the `as_tuple` parameter.

Fixes #8772

* ➖ Don't install `typing-extensions` in Python 3.8+

After more detailed analysis of how to implement Python version-specific
type annotations using SpaCy, it has been determined that by branching
on a comparison against `sys.version_info` can be statically analyzed by
Mypy well enough to enable us to conditionally use
`typing_extensions.Literal`. This means that we no longer need to
install `typing_extensions` for Python versions greater than or equal to
3.8! 🎉

These changes revert previous changes installing `typing-extensions`
regardless of Python version and modify how we import the `Literal` type
to ensure that Mypy treats it properly.

* resolve mypy errors for Strict pydantic types

* refactor code to avoid missing return statement

* fix types of convert CLI command

* avoid list-set confustion in debug_data

* fix typo and formatting

* small fixes to avoid type ignores

* fix types in profile CLI command and make it more efficient

* type fixes in projects CLI

* put one ignore back

* type fixes for render

* fix render types - the sequel

* fix BaseDefault in language definitions

* fix type of noun_chunks iterator - yields tuple instead of span

* fix types in language-specific modules

* 🏷 Expand accepted inputs of `get_string_id`

`get_string_id` accepts either a string (in which case it returns its 
ID) or an ID (in which case it immediately returns the ID). These 
changes extend the type annotation of `get_string_id` to indicate that 
it can accept either strings or IDs.

* 🏷 Handle override types in `combine_score_weights`

The `combine_score_weights` function allows users to pass an `overrides` 
mapping to override data extracted from the `weights` argument. Since it 
allows `Optional` dictionary values, the return value may also include 
`Optional` dictionary values.

These changes update the type annotations for `combine_score_weights` to 
reflect this fact.

* 🏷 Fix tokenizer serialization method signatures in `DummyTokenizer`

* 🏷 Fix redefinition of `wandb_logger`

These changes fix the redefinition of `wandb_logger` by giving a 
separate name to each `WandbLogger` version. For 
backwards-compatibility, `spacy.train` still exports `wandb_logger_v3` 
as `wandb_logger` for now.

* more fixes for typing in language

* type fixes in model definitions

* 🏷 Annotate `_RandomWords.probs` as `NDArray`

* 🏷 Annotate `tok2vec` layers to help Mypy

* 🐛 Fix `_RandomWords.probs` type annotations for Python 3.6

Also remove an import that I forgot to move to the top of the module 😅

* more fixes for matchers and other pipeline components

* quick fix for entity linker

* fixing types for spancat, textcat, etc

* bugfix for tok2vec

* type annotations for scorer

* add runtime_checkable for Protocol

* type and import fixes in tests

* mypy fixes for training utilities

* few fixes in util

* fix import

* 🐵 Remove unused `# type: ignore` directives

* 🏷 Annotate `Language._components`

* 🏷 Annotate `spacy.pipeline.Pipe`

* add doc as property to span.pyi

* small fixes and cleanup

* explicit type annotations instead of via comment

Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
Co-authored-by: svlandeg <svlandeg@github.com>
2021-10-14 15:21:40 +02:00

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---
title: Vocab
teaser: A storage class for vocabulary and other data shared across a language
tag: class
source: spacy/vocab.pyx
---
The `Vocab` object provides a lookup table that allows you to access
[`Lexeme`](/api/lexeme) objects, as well as the
[`StringStore`](/api/stringstore). It also owns underlying C-data that is shared
between `Doc` objects.
## Vocab.\_\_init\_\_ {#init tag="method"}
Create the vocabulary.
> #### Example
>
> ```python
> from spacy.vocab import Vocab
> vocab = Vocab(strings=["hello", "world"])
> ```
| Name | Description |
| ------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `lex_attr_getters` | A dictionary mapping attribute IDs to functions to compute them. Defaults to `None`. ~~Optional[Dict[str, Callable[[str], Any]]]~~ |
| `strings` | A [`StringStore`](/api/stringstore) that maps strings to hash values, and vice versa, or a list of strings. ~~Union[List[str], StringStore]~~ |
| `lookups` | A [`Lookups`](/api/lookups) that stores the `lexeme_norm` and other large lookup tables. Defaults to `None`. ~~Optional[Lookups]~~ |
| `oov_prob` | The default OOV probability. Defaults to `-20.0`. ~~float~~ |
| `vectors_name` <Tag variant="new">2.2</Tag> | A name to identify the vectors table. ~~str~~ |
| `writing_system` | A dictionary describing the language's writing system. Typically provided by [`Language.Defaults`](/api/language#defaults). ~~Dict[str, Any]~~ |
| `get_noun_chunks` | A function that yields base noun phrases used for [`Doc.noun_chunks`](/api/doc#noun_chunks). ~~Optional[Callable[[Union[Doc, Span], Iterator[Tuple[int, int, int]]]]]~~ |
## Vocab.\_\_len\_\_ {#len tag="method"}
Get the current number of lexemes in the vocabulary.
> #### Example
>
> ```python
> doc = nlp("This is a sentence.")
> assert len(nlp.vocab) > 0
> ```
| Name | Description |
| ----------- | ------------------------------------------------ |
| **RETURNS** | The number of lexemes in the vocabulary. ~~int~~ |
## Vocab.\_\_getitem\_\_ {#getitem tag="method"}
Retrieve a lexeme, given an int ID or a string. If a previously unseen string is
given, a new lexeme is created and stored.
> #### Example
>
> ```python
> apple = nlp.vocab.strings["apple"]
> assert nlp.vocab[apple] == nlp.vocab["apple"]
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------ |
| `id_or_string` | The hash value of a word, or its string. ~~Union[int, str]~~ |
| **RETURNS** | The lexeme indicated by the given ID. ~~Lexeme~~ |
## Vocab.\_\_iter\_\_ {#iter tag="method"}
Iterate over the lexemes in the vocabulary.
> #### Example
>
> ```python
> stop_words = (lex for lex in nlp.vocab if lex.is_stop)
> ```
| Name | Description |
| ---------- | -------------------------------------- |
| **YIELDS** | An entry in the vocabulary. ~~Lexeme~~ |
## Vocab.\_\_contains\_\_ {#contains tag="method"}
Check whether the string has an entry in the vocabulary. To get the ID for a
given string, you need to look it up in
[`vocab.strings`](/api/vocab#attributes).
> #### Example
>
> ```python
> apple = nlp.vocab.strings["apple"]
> oov = nlp.vocab.strings["dskfodkfos"]
> assert apple in nlp.vocab
> assert oov not in nlp.vocab
> ```
| Name | Description |
| ----------- | ----------------------------------------------------------- |
| `string` | The ID string. ~~str~~ |
| **RETURNS** | Whether the string has an entry in the vocabulary. ~~bool~~ |
## Vocab.add_flag {#add_flag tag="method"}
Set a new boolean flag to words in the vocabulary. The `flag_getter` function
will be called over the words currently in the vocab, and then applied to new
words as they occur. You'll then be able to access the flag value on each token,
using `token.check_flag(flag_id)`.
> #### Example
>
> ```python
> def is_my_product(text):
> products = ["spaCy", "Thinc", "displaCy"]
> return text in products
>
> MY_PRODUCT = nlp.vocab.add_flag(is_my_product)
> doc = nlp("I like spaCy")
> assert doc[2].check_flag(MY_PRODUCT) == True
> ```
| Name | Description |
| ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `flag_getter` | A function that takes the lexeme text and returns the boolean flag value. ~~Callable[[str], bool]~~ |
| `flag_id` | An integer between `1` and `63` (inclusive), specifying the bit at which the flag will be stored. If `-1`, the lowest available bit will be chosen. ~~int~~ |
| **RETURNS** | The integer ID by which the flag value can be checked. ~~int~~ |
## Vocab.reset_vectors {#reset_vectors tag="method" new="2"}
Drop the current vector table. Because all vectors must be the same width, you
have to call this to change the size of the vectors. Only one of the `width` and
`shape` keyword arguments can be specified.
> #### Example
>
> ```python
> nlp.vocab.reset_vectors(width=300)
> ```
| Name | Description |
| -------------- | ---------------------- |
| _keyword-only_ | |
| `width` | The new width. ~~int~~ |
| `shape` | The new shape. ~~int~~ |
## Vocab.prune_vectors {#prune_vectors tag="method" new="2"}
Reduce the current vector table to `nr_row` unique entries. Words mapped to the
discarded vectors will be remapped to the closest vector among those remaining.
For example, suppose the original table had vectors for the words:
`['sat', 'cat', 'feline', 'reclined']`. If we prune the vector table to, two
rows, we would discard the vectors for "feline" and "reclined". These words
would then be remapped to the closest remaining vector – so "feline" would have
the same vector as "cat", and "reclined" would have the same vector as "sat".
The similarities are judged by cosine. The original vectors may be large, so the
cosines are calculated in minibatches to reduce memory usage.
> #### Example
>
> ```python
> nlp.vocab.prune_vectors(10000)
> assert len(nlp.vocab.vectors) <= 1000
> ```
| Name | Description |
| ------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `nr_row` | The number of rows to keep in the vector table. ~~int~~ |
| `batch_size` | Batch of vectors for calculating the similarities. Larger batch sizes might be faster, while temporarily requiring more memory. ~~int~~ |
| **RETURNS** | A dictionary keyed by removed words mapped to `(string, score)` tuples, where `string` is the entry the removed word was mapped to, and `score` the similarity score between the two words. ~~Dict[str, Tuple[str, float]]~~ |
## Vocab.get_vector {#get_vector tag="method" new="2"}
Retrieve a vector for a word in the vocabulary. Words can be looked up by string
or hash value. If no vectors data is loaded, a `ValueError` is raised. If `minn`
is defined, then the resulting vector uses [FastText](https://fasttext.cc/)'s
subword features by average over n-grams of `orth` (introduced in spaCy `v2.1`).
> #### Example
>
> ```python
> nlp.vocab.get_vector("apple")
> nlp.vocab.get_vector("apple", minn=1, maxn=5)
> ```
| Name | Description |
| ----------------------------------- | ---------------------------------------------------------------------------------------------------------------------- |
| `orth` | The hash value of a word, or its unicode string. ~~Union[int, str]~~ |
| `minn` <Tag variant="new">2.1</Tag> | Minimum n-gram length used for FastText's n-gram computation. Defaults to the length of `orth`. ~~int~~ |
| `maxn` <Tag variant="new">2.1</Tag> | Maximum n-gram length used for FastText's n-gram computation. Defaults to the length of `orth`. ~~int~~ |
| **RETURNS** | A word vector. Size and shape are determined by the `Vocab.vectors` instance. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
## Vocab.set_vector {#set_vector tag="method" new="2"}
Set a vector for a word in the vocabulary. Words can be referenced by string or
hash value.
> #### Example
>
> ```python
> nlp.vocab.set_vector("apple", array([...]))
> ```
| Name | Description |
| -------- | -------------------------------------------------------------------- |
| `orth` | The hash value of a word, or its unicode string. ~~Union[int, str]~~ |
| `vector` | The vector to set. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
## Vocab.has_vector {#has_vector tag="method" new="2"}
Check whether a word has a vector. Returns `False` if no vectors are loaded.
Words can be looked up by string or hash value.
> #### Example
>
> ```python
> if nlp.vocab.has_vector("apple"):
> vector = nlp.vocab.get_vector("apple")
> ```
| Name | Description |
| ----------- | -------------------------------------------------------------------- |
| `orth` | The hash value of a word, or its unicode string. ~~Union[int, str]~~ |
| **RETURNS** | Whether the word has a vector. ~~bool~~ |
## Vocab.to_disk {#to_disk tag="method" new="2"}
Save the current state to a directory.
> #### Example
>
> ```python
> nlp.vocab.to_disk("/path/to/vocab")
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
## Vocab.from_disk {#from_disk tag="method" new="2"}
Loads state from a directory. Modifies the object in place and returns it.
> #### Example
>
> ```python
> from spacy.vocab import Vocab
> vocab = Vocab().from_disk("/path/to/vocab")
> ```
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified `Vocab` object. ~~Vocab~~ |
## Vocab.to_bytes {#to_bytes tag="method"}
Serialize the current state to a binary string.
> #### Example
>
> ```python
> vocab_bytes = nlp.vocab.to_bytes()
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The serialized form of the `Vocab` object. ~~Vocab~~ |
## Vocab.from_bytes {#from_bytes tag="method"}
Load state from a binary string.
> #### Example
>
> ```python
> from spacy.vocab import Vocab
> vocab_bytes = nlp.vocab.to_bytes()
> vocab = Vocab()
> vocab.from_bytes(vocab_bytes)
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `Vocab` object. ~~Vocab~~ |
## Attributes {#attributes}
> #### Example
>
> ```python
> apple_id = nlp.vocab.strings["apple"]
> assert type(apple_id) == int
> PERSON = nlp.vocab.strings["PERSON"]
> assert type(PERSON) == int
> ```
| Name | Description |
| ---------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `strings` | A table managing the string-to-int mapping. ~~StringStore~~ |
| `vectors` <Tag variant="new">2</Tag> | A table associating word IDs to word vectors. ~~Vectors~~ |
| `vectors_length` | Number of dimensions for each word vector. ~~int~~ |
| `lookups` | The available lookup tables in this vocab. ~~Lookups~~ |
| `writing_system` <Tag variant="new">2.1</Tag> | A dict with information about the language's writing system. ~~Dict[str, Any]~~ |
| `get_noun_chunks` <Tag variant="new">3.0</Tag> | A function that yields base noun phrases used for [`Doc.noun_chunks`](/ap/doc#noun_chunks). ~~Optional[Callable[[Union[Doc, Span], Iterator[Tuple[int, int, int]]]]]~~ |
## Serialization fields {#serialization-fields}
During serialization, spaCy will export several data fields used to restore
different aspects of the object. If needed, you can exclude them from
serialization by passing in the string names via the `exclude` argument.
> #### Example
>
> ```python
> data = vocab.to_bytes(exclude=["strings", "vectors"])
> vocab.from_disk("./vocab", exclude=["strings"])
> ```
| Name | Description |
| --------- | ----------------------------------------------------- |
| `strings` | The strings in the [`StringStore`](/api/stringstore). |
| `vectors` | The word vectors, if available. |
| `lookups` | The lookup tables, if available. |
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